Unlocking Customer Insights: A Review of Sentiment Analysis Approaches for Product Reviews with Performance Benchmarking.

The exponential growth of user-generated content on e-commerce platforms has emphasized the critical role of  sentiment analysis for interpreting consumer feedback. This paper provides a systematic review of sentiment  analysis techniques, categorizing them into lexicon-based methods, machine learning algorithms, deep learning  architectures, and hybrid or ensemble strategies. Each method is evaluated based on its theoretical framework,  suitability for analyzing product reviews, and comparative performance across diverse datasets. Emphasis is  placed on deep learning and ensemble models due to their capacity to capture contextual and semantic  complexities more effectively. Performance benchmarks drawn from existing literature highlight key trends,  limitations, and methodological innovations in sentiment classification. These insights support the development  of robust and scalable sentiment analysis systems for product review interpretation.